Classification by instance-based learning algorithm

14Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The basic k-nearest-neighbor classification algorithm works well in many domains but has several shortcomings. This paper proposes a tolerant instance-based learning algorithm TIBL and it's combining method by simple voting of TIBL, which is an integration of genetic algorithm, tolerant rough sets and k-nearest neighbor classification algorithm. The proposed algorithms seek to reduce storage requirement and increase generalization accuracy when compared to the basic k-nearest neighbor algorithm and other learning models. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository. The results show that TIBL algorithm and it's combining method, improve the performance of the k-nearest neighbor classification, and also achieves higher generalization accuracy than other popular machine learning algorithms. © Springer-Verlag Berlin Heidelberg 2005.

Cite

CITATION STYLE

APA

Bao, Y., Tsuchiya, E., Ishii, N., & Du, X. (2005). Classification by instance-based learning algorithm. In Lecture Notes in Computer Science (Vol. 3578, pp. 133–140). Springer Verlag. https://doi.org/10.1007/11508069_18

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free